The present invention relates to object detection and tracking, and, in particular, to an apparatus and method for resource-adaptive object detection and tracking.
Object detection and in particular face detection became very popular in recent years. It enables, for instance, cameras to focus on the faces in the image or to adapt the camera settings accordingly. Due to the restricted processing power of embedded devices, such systems can only detect a limited number of faces or detect the faces only with low frame rates in images with high resolutions. These restrictions pose no problems for digital cameras, because it suffices to detect only the most prominent faces in the image. However, some applications necessitate a system that detects all objects of interest in the image with high frame rates and small object sizes as well. Current systems cannot afford all these requirements simultaneously on hardware with low processing power, even when using fast object detection algorithms. The most famous method for rapid object detection has been published by Viola and Jones (P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 511-518, Kauai, HI, USA, April 2001; and P. Viola and M. Jones, “Robust real-time object detection,” International Journal of Computer Vision, 57(2):137-154, 2002]. Many variants or different methods have been published by other authors (see A. Ernst, T. Ruf, and C. Kueblbeck, “A modular framework to detect and analyze faces for audience measurement systems,” In 2nd Workshop on Pervasive Advertising, pages 75-87, Luebeck, 2009; B. Froeba and C. Kueblbeck, “Robust face detection at video frame rate based on edge orientation features,” In IEEE International Conference on Automatic Face and Gesture Recognition, pages 342-347, Los Alamitos, Calif., USA, 2002; B. Froeba, “Verfahren zur Echtzeit-Gesichtsdetektion in Grauwertbildern,” Shaker, 2003; B. Froeba and A. Ernst, “Fast frontal-view face detection using a multi-path decision tree,” In Audio- and Video-Based Biometric Person Authentication, pages 921-928, 2003; C. Kueblbeck and A. Ernst, “Face detection and tracking in video sequences using the modified census transformation,” In Image and Vision Computing, 24(6):564-572, 2006; R. Lienhart and J. Maydt, “An extended set of haar-like features for rapid object detection,” In IEEE ICIP, volume 1, pages 900-903, 2002; H. A. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1):23-38, 1998; and B. Wu, A. Haizhou, H. Chang, and L. Shihong, “Fast rotation invariant multi-view face detection based on Real Adaboost,” In Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pages 79-84, 2004). In US Patent Application Publication No. 2009/0169067 A1 published Jul. 2, 2009, a face detection and tracking method is executed by a computer or a microprocessor with computing capability for identifying human faces and positions thereof in image frames.
However, it would be appreciated if more efficient concepts would be provided.